Sr. Data Engineer

Orion180 Insurance Services•Irving, TX

2d•Onsite

About The Position

We are actively recruiting for a Sr. Data Engineer to join our growing team! We believe analytics isn't a back-office function. It's not a reporting layer bolted onto the business after the real decisions get made. It's the foundation that tells you where risk actually lives, where growth is hiding, and where you're missing opportunities before it's too late to do anything about it. That belief is driving real investment in our people, platforms, and in how deeply analytics is woven into the way we operate. From underwriting to distribution to customer experience, we're building a data culture where insight drives action. It also means evolving how we think about the role of a Data Engineer. The expectation isn’t just technical execution. It’s curiosity. Ownership. The ability to step into ambiguity, frame the problem, and help guide the business toward better decisions. ABOUT THE ROLE: In this role you will be a hands-on technical leader designing, building, and optimizing the data integrations, cloud data infrastructure, and data models that power analytics and data science across the company. You will lead the development of robust data pipelines, orchestrate complex workflows using Azure native tools, and implement data governance frameworks. You will collaborate with data scientists, analysts, and business leaders to turn raw data into scalable, production-ready assets for real-time analytics engines, portfolio risk platforms, and client-facing digital products.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field
United States Citizen or Green Card holder required
6+ years of dedicated experience in data engineering, with at least 3 years focused on the Azure ecosystem within a transactional, highly regulated environment.
Mastery of T-SQL and advanced relational database concepts, alongside strong scripting proficiency in Python (or PySpark).
Extensive hands-on experience building complex control flows, mapping data flows, and parameterizing pipelines in Azure Data Factory.
Proven experience processing large-scale data using Spark clusters within Azure Databricks.
Deep understanding of Azure Data Lake Storage (ADLS Gen2), Delta Lake formatting, and dedicated SQL pools in Azure Synapse.
Strong experience using Azure DevOps (Azure Pipelines, Repos) for version control and deploying data engineering workloads.
Excellent analytical, problem-solving, and critical-thinking skills.
Strong communication and collaboration abilities across technical and non-technical teams.
Ability to manage multiple projects in a fast-paced, results-driven environment

Nice To Haves

Experience or familiarity with semantic data modeling and governed metric layers
Working within scalable cloud data platforms (e.g., Snowflake, Databricks)
Integrating analytics within broader data engineering workflows
Exposure to emerging capabilities such as AI-assisted analysis, automated insight generation, and advanced data visualization techniques

Responsibilities

Build and maintain scalable, fault-tolerant ETL/ELT pipelines using Azure Data Factory, Synapse, and Databricks to ingest diverse financial datasets.
Create and optimize data models (dimensional, medallion/bronze-silver-gold) to support PowerBI semantic layers and downstream analytics.
Implement automated data validation, lineage tracking, and monitoring frameworks to ensure the highest standards of data security, privacy, and regulatory compliance.
Develop real-time data streaming and event-driven architecture using Azure Event Hubs and Azure Stream Analytics for instant fraud detection and transaction monitoring.
Engineer and implement highly optimized Lakehouse architectures utilizing Azure Synapse Analytics and Microsoft Fabric, ensuring efficient storage and querying of multi-terabyte data layers.
Design optimized relational, dimensional, and graph data models for diverse analytics computing and workloads.
Manage, optimize, and tune large-scale Azure SQL Databases and Managed Instances, writing complex, highly performant stored procedures and T-SQL queries.
Establish robust CI/CD deployment pipelines for all data assets using Azure DevOps, ensuring automated testing, validation, and infrastructure-as-code.
Collaborate with data scientists to productionalize ML feature pipelines and support MLOps workflows.
Implement strict data masking, row-level security, and data lineage workflows using Microsoft Purview to comply with financial regulations.
Act as a technical leader, mentoring junior engineers and promoting modern Agile DataOps engineering practices: CI/CD for data pipelines, testing, version control and code review.